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ISACA AAIA Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI Operations	46%	- Operational Controls and Readiness - Performance Monitoring and Evaluation - Third-Party and Supply Chain Risk - AI System Lifecycle and Deployment - Incident Management and Resilience
Topic 2: AI Governance and Risk	33%	- AI Models, Considerations, and Requirements - Ethics, Regulations, and Standards for AI - Privacy and Data Governance Programs - AI Risk Management - AI Governance and Program Management
Topic 3: AI Auditing Tools and Techniques	21%	- Data Quality and Analytics for AI Audit - AI Audit Outputs and Reporting - Audit Testing and Sampling Methodologies - Audit Planning and Design - Audit Evidence Collection Techniques

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ISACA Advanced in AI Audit Sample Questions (Q13-Q18):

NEW QUESTION # 13
An IS auditor reviewing the latest AI chatbot release identifies that, despite high accuracy rates, non-English users complain about the model ' s poor accuracy. Which of the following controls is BEST at ensuring detection of subgroup regressions?

A. Human review for non-English languages after go-live
B. Weighted metric with higher accuracy targets
C. Translate all outputs to English and evaluate in English for consistency
D. Comparative language evaluations with parity thresholds

Answer: D

Explanation:
A common pitfall in AI performance is " aggregate accuracy, " which masks poor performance in specific demographics or subgroups. To detect these " subgroup regressions, " auditors should look for " comparative evaluations " where the model is tested separately for different languages. " Parity thresholds " establish the maximum allowable difference in performance between the majority group (English) and minority groups.
This ensures fairness and consistent user experience. Post-go-live human review is reactive, and translating everything to English ignores the nuances of the original language that likely caused the errors in the first place.

NEW QUESTION # 14
For a sales promotion, an AI system sorts customer attributes into several categories by analyzing transaction history. Verifying which of the following would BEST validate the effectiveness of this process?

A. Sensitive attributes are converted to other data types prior to input.
B. Sampling of AI output is conducted to identify unusual decisions.
C. Stress tests are regularly conducted to maintain consistent AI performance.
D. The applied methodology adequately reflects business objectives.

Answer: D

NEW QUESTION # 15
The BEST way to prevent sensitive information disclosure by large language model (LLM) chatbots is through:

A. Access controls
B. Data sanitization
C. Manual monitoring
D. Data masking

Answer: D

NEW QUESTION # 16
Which of the following should be of GREATEST concern to an IS auditor when reviewing ethical considerations for an AI solution?

A. The solution documentation is still in draft.
B. The model has not been retrained recently.
C. The solution is hosted on a shared cloud environment.
D. The decision-making process is unexplainable.

Answer: D

Explanation:
The GREATEST concern is when the AI system's decision-making process is unexplainable (A), especially for high-impact or regulated decisions. AAIA stresses that explainability is essential for accountability, fairness assessments, compliance, and public trust. If decisions cannot be explained, the organization cannot validate fairness, detect bias, or justify outcomes to regulators or affected individuals.

NEW QUESTION # 17
Which of the following BEST describes the auditor's role when reviewing an organization's AI governance maturity?

A. Approving all new AI projects before development begins
B. Building the AI models being audited
C. Independently assessing whether governance structures, policies, and controls are designed and operating effectively across the AI lifecycle
D. Managing the organization's data science team

Answer: C

Explanation:
The auditor's role is independent assurance -- evaluating whether governance, risk, and control structures exist and function effectively -- not operational involvement in building or approving AI systems.

NEW QUESTION # 18
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